Abstract:

One of the most challenging issues emerging universities are facing is the establishment of fair and transparent invigilation schedules. These universities often have a multi-campus distribution and limited staffing capacity. As a result, many faculty members commute long distances, balance multiple teaching and administrative responsibilities, and manage specific time preferences to accommodate personal circumstances. Therefore, traditional invigilation slot allocation becomes increasingly complex. It is often not aligned with institutional policies, leading to unequal workload distribution and employees’ dissatisfaction. 

The aim of the present study is to propose an AI-driven scheduling framework to support equitable, efficient, and policy-compliant invigilation schedules in emerging universities. Key factors including teaching load, campus location, commuting constraints, faculty availability, historical participation, and the university’s invigilation policy rules are incorporated in the model. Through optimization and fairness constraints, the tool is expected to generate a more balanced invigilation schedule that is bias-free, fair, and objective. It will reduce the administrative burden while allowing planners to accommodate individual circumstances where possible in a fair manner.

Essentially, the proposed framework will present the conceptual design, the data requirements, the system architecture, and the expected institutional impact. Further, it would provide a practical solution that would allow emerging universities to enhance the fairness and transparency of their invigilation allocation process.

Keywords:

Artificial intelligence, Invigilation allocation, Emerging universities.